In recent years, face recognition in a video has become an active research aspect in the field of face recognition. The technique for face recognition in a video has a wide application prospect in various aspects, such as security monitoring, intelligent identity verification, home entertainment or the like. Generally, the face recognition in the video refers to performing feature extraction on face areas detected in respective image frames of the video, comparing the features extracted from the respective frames with an existing face database, and finally recognizing identity of the face based on the comparison result.
According to existing methods for the face recognition in the video, image frames in a video are usually taken out frame by frame, then the faces in respective image frames are respectively compared with a face database and recognized, and when the comparison and recognition result of the respective frames satisfies a predetermined condition (for example, one same person is recognized in consecutive five frames), it is determined that a face is recognized. However, this recognition method is a static one and depends on independent comparison results of the respective frame images. Therefore, when some of the image frames in the video do not have high quality, the detection results thereof are not accurate, or the angles of the faces therein do not have corresponding samples in the face database, the accuracy of the recognition will be affected, resulting in low recognition accuracy.